The R Interface to 'SyncroSim'

'SyncroSim' is a generalized framework for managing scenario-based datasets (< https://syncrosim.com/>). 'rsyncrosim' provides an interface to 'SyncroSim'. Simulation models can be added to 'SyncroSim' in order to transform these datasets, taking advantage of general features such as defining scenarios of model inputs, running Monte Carlo simulations, and summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 2.3.5 or higher (API documentation: < https://docs.syncrosim.com/>).

Reliably Return the Source and Call Location of a Command

Robust and reliable functions to return informative outputs to console with the run or source location of a command. This can be from the 'RScript'/R terminal commands or 'RStudio' console, source editor, 'Rmarkdown' document and a Shiny application.

Nonparametric Models for Longitudinal Data

Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods for the conditional-mean and conditional-distribution based models with longitudinal Data.

Power Analyses for Interaction Effects in Cross-Sectional Regressions

Power analysis for regression models which test the interaction of two or three independent variables on a single dependent variable. Includes options for correlated interacting variables and specifying variable reliability. Two-way interactions can include continuous, binary, or ordinal variables. Power analyses can be done either analytically or via simulation. Includes tools for simulating single data sets and visualizing power analysis results. The primary functions are power_interaction_r2() and power_interaction() for two-way interactions, and power_interaction_3way_r2() for three-way interactions. Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM (2023). "Tutorial: Power analyses for interaction effects in cross-sectional regressions." .

Tau-Leaping Stochastic Simulation

Implements adaptive tau leaping to approximate the trajectory of a continuous-time stochastic process as described by Cao et al. (2007) The Journal of Chemical Physics (aka. the Gillespie stochastic simulation algorithm). This package is based upon work supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and NIH R01-AI049334 to Rustom Antia.

Lexicons for Text Analysis

A collection of lexical hash tables, dictionaries, and word lists.

Trajectories and Phylogenies Simulator

Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) .

Simulate from ODE-Based Models

Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.

Tools for Modelling of Animal Flight Performance

Allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model described in Klein Heerenbrink et al. (2015) . Taking inspiration from the program 'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. 'afpt' can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions.

Single Cell Transcriptomics-Level Cytokine Activity Prediction and Estimation

Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the 'CytoSig' and the 'Reactome' databases and scored using the modified 'Variance-adjusted Mahalanobis (VAM)' framework for single-cell RNA-sequencing (scRNA-seq) data. 'CytoSig' database is described in: Jiang at al., (2021) . 'Reactome' database is described in: Gillespie et al., (2021) . The 'VAM' method is outlined in: Frost (2020) .